On Editing Dictionaries for Uralic Languages in an Online Environment

Alnajjar, Khalid; Hämäläinen, Mika; Rueter, Jack

doi:10.18653/v1/2020.iwclul-1.4

Cited by 5 publications

(3 citation statements)

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“…At the current, stage our dictionary editing system, Ve rdd [4,3], contains words for multiple endangered languages and their translations in a graph structure. This data could be extended by predicting new relations into the graph with semantic models such as word embeddings.…”

Section: Discussionmentioning

confidence: 99%

“…Jack's enthusiasm and dedication to endangered languages is clearly shown in all the various dictionaries and FSTs built and maintained by him. He supervised my work on building the dictionary editing system, Ve rdd [4,3]. He was always available for discussing and supporting my work, without him Ve rdd would not be in the great level it is at at the moment.…”

Section: Acknowledgementmentioning

confidence: 99%

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When Word Embeddings Become Endangered

Alnajjar

2021

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Big languages such as English and Finnish have many natural language processing (NLP) resources and models, but this is not the case for low-resourced and endangered languages as such resources are so scarce despite the great advantages they would provide for the language communities. The most common types of resources available for low-resourced and endangered languages are translation dictionaries and universal dependencies. In this paper, we present a method for constructing word embeddings for endangered languages using existing word embeddings of different resource-rich languages and the translation dictionaries of resource-poor languages. Thereafter, the embeddings are fine-tuned using the sentences in the universal dependencies and aligned to match the semantic spaces of the big languages; resulting in crosslingual embeddings. The endangered languages we work with here are Erzya, Moksha, Komi-Zyrian and Skolt Sami. Furthermore, we build a universal sentiment analysis model for all the languages that are part of this study, whether endangered or not, by utilizing cross-lingual word embeddings. The evaluation conducted shows that our word embeddings for endangered languages are well-aligned with the resource-rich languages, and they are suitable for training task-specific models as demonstrated by our sentiment analysis model which achieved a high accuracy. All our cross-lingual word embeddings and the sentiment analysis model have been released openly via an easy-to-use Python library.

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Section: Discussionmentioning

confidence: 99%

Section: Acknowledgementmentioning

confidence: 99%

When Word Embeddings Become Endangered

Alnajjar

2021

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“…We present an open-source dictionary editing tool 1 called Ve rdd 2 . The tool has been and currently is under active development to cater for the needs of Skolt Sami (ISO 639-2: sms) speaking language community and their on-going project on modernizing a Finnish-Skolt Sami paper dictionary (see (Alnajjar et al, 2020)). Although Skolt Sami is severely endangered with its 300 native speakers (Moseley, 2010), a great deal of NLP tools have been developed for it over the past decade; such as finite-state based morphological analysers and generators in the GiellaLT repository (Moshagen et al, 2014), XML and MediaWiki based online dictionary (Rueter and Hämäläinen, 2017) and most recently a universal dependency treebank (Nivre et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Ve'rdd. Narrowing the Gap between Paper Dictionaries, Low-Resource NLP and Community Involvement

Alnajjar¹,

Hämäläinen²,

Rueter³

et al. 2020

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We present an open-source online dictionary editing system, Ve rdd, that offers a chance to reevaluate and edit grassroots dictionaries that have been exposed to multiple amateur editors. The idea is to incorporate community activities into a state-of-the-art finite-state language description of a seriously endangered minority language, Skolt Sami. Problems involve getting the community to take part in things above the pencil-and-paper level. At times, it seems that the native speakers and the dictionary oriented are lacking technical understanding to utilize the infrastructures which might make their work more meaningful in the future, i.e. multiple reuse of all of their input. Therefore, our system integrates with the existing tools and infrastructures for Uralic language masking the technical complexities behind a user-friendly UI.

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